Wheat Data Interoperability WG outputs The Wheat Initiative

Wheat Data Interoperability WG outputs

The Wheat Initiative § Created in 2011 following endorsement by G 20 Agriculture Ministries to improve food security § A framework to identify synergies and facilitate collaborations for wheat improvement at the international level § The Wheat Initiative members § Countries: Argentina, Australia, Brazil, Canada, China, France, Germany, Hungary, India, Ireland, Italy, Japan, Spain, Turkey, UK, USA § International organizations: CIMMYT, ICARDA § Private companies: Arvalis, Bayer Crop. Science, Florimond Desprez V&F, KWS UK, Limagrain, Monsanto Company, RAGT 2 n Saateen Union Research, Syngenta Crop Protection 2

The problem Data manager, data provider Top management or breeders • What are the sources of resistance to stem rust (UG 99) and tolerance to drought conditions in bread wheat? Data are Dispersed Heretogeneous Abundant • I want to make my data findable, reusable and linkable to other data: • What ontologies and metadata elements are commonly used to describe the types of data I am dealing with? • What data formats could I use to share my data • Where could I deposit my data? Data scientists, bioinformaticians Phenotypic data • How do I extract information from all these databases? • Do I have welldocumented metadata to make queries? • How do I link the data to get smarter information? Gene sequences Passport information Low density markers Phenotypic data

The deliverables § Guidelines (http: //wheatis. org/Data. Standards. php) § Data exchange formats § Example: VCF (Variant Call Format) for sequence variation data, GFF 3 for genome annotation data, etc. § Data description best practices § Consistent use of ontologies, consistent use of external database cross references § Data sharing best practices § Share data matrices along with relevant metadata (example: trait along with method, units and scales or environmental ones) § Useful tools and use cases that highlight data formats and vocabularies issues § A portal of wheat related ontologies and vocabularies (http: //wheat. agroportal. lirmm. fr/ontologies) § Allows the access to the ontologies and vocabularies through APIs. § A prototype § Implementation of use cases of wheat data integration within the Agro. LD (Agronomic Linked Data) tool: http: //volvestre. cirad. fr: 8080/agrold/

The deliverables

Benefits for many target users As a data producer or manager • Easily conform to the well-recognized data repositories and facilitate the deposit of your data within these repositories; • Share common meanings of the words you utilize to describe your data and make your data more machine-readable and computable • Contribute to foster the development of smarter search tools and make your data more visible and discoverable As a wheat related information system or tool developer • Basing your tool or information system on the recommended data formats and vocabularies will make it easier to integrate data from various data sources, deliver smarter outputs for a wider audience As a wheat related ontology developer • Share your ontologies through the WDI wheat ontologies portal and make them more visible to the community • Reuse or link your ontologies to existing concepts and terms in wheat related ontologies to enrich them, make them more visible and in some cases save you time.

How You Can Endorse the guidelines on data formats 7 § For legacy data § Please provide your data in at least one of the recommended data formats even if, for some reasons, you need to also keep them in other non-recommended formats § For future developments § Please consider using the recommended data formats from the beginning. § Example: provide your sequence variation data in the latest VCF file format § Please refer to the WDI guidelines for precise recommendations on each data type

How You Can Endorse the data description and sharing best practices 8 § Describe your data following the WDI recommendations and with the recommended vocabularies. § Examples: § For genome annotation data in GFF 3 format, use of ontologies for functional annotation in column 9, such as, Gene Ontology and Sequence Ontology. § For observation Variables (including trait and environment variables), use existing variables, listed in the following vocabularies and ontologies : § § Wheat crop ontology INRA Wheat Ontology Biorefinery ontology XEO, XEML Environment Ontology

How You Can Endorse the WDI wheat related ontologies portal? 9 § Share your wheat related ontologies within the WDI slice in Agroportal § Before developing a new ontology § Make sure there is not an existing one within the WDI slice in Agroportal that covers your needs § When developing a new ontology § Please reuse or link to exiting concepts and terms in the ontologies within the WDI slice in Agroportal whenever possible. § Whenever possible § Please align your ontologies to the existing ones within the WDI slice in Agroportal and share the mapping results

Endorsements/Adopters Laboratory Contact NIAB, www. niab. com Professor Mario Caccamo Head of Crop Bioinformatics Doreen Ware Adjunct Associate Professor Ph. D. , Ohio State University USDA ARS and Cold Spring Harbor Laboratory, http: //cshl. edu/ 10 Paul Kersey EMBL European Bioinformatics Institute, http: //www. ebi. ac. uk/ Team Leader Non-vertebrate Genomics Australian Center for Plant Functional Genomics, http: //www. acpfg. com. au/ The Genome Analysis Center, http: //www. tgac. uk/ Dr Baumann, Ute Bioinformatics Leader Robert Davey Data Infrastructure & Algorithms Group Leader Munich Information Center for Protein Sequences (MIPS), Helmholtz Center Munich, http: //www. helmholtzmuenchen. de/ Dr. Klaus Mayer Research Director MIPS INRA URGI, https: //urgi. versailles. inra. fr/ Michael Alaux, Deputy leader of "Information System and data integration" team Cyril Pommier, Deputy leader, Information System and Data integration team, Phenotype thematic leader Rothamsted Research, http: //www. rothamsted. ac. uk/ Christopher Rawlings Head of Department Computational & Systems Biology Harpenden James Hutton Institute, http: //www. hutton. ac. uk/ David Marshall Information and Computational Sciences The James Hutton Institute Richard Allan James, Head of Knowledge Management Rosemary Shrestha, Data Coordinator CIMMYT Wheat program, http: //www. cimmyt. org/en/

The Wheat Data Interoperability WG 11 § Aims: contribute to the improvement of Wheat related data interoperability by § Building a common interoperability framework (metadata, data formats and vocabularies) § Providing guidelines for describing, representing and linking Wheat related data Contributors Sponsors Contributors: Alaux Michael (INRA, France), Aubin Sophie (INRA, France), Arnaud Elizabeth (Bioversity, France), Baumann Ute (Adelaide Uni, Australia), Buche Patrice (INRA, France), Cooper Laurel (Planteome, USA), Fulss Richard (CIMMYT, Mexico), Hologne Odile (INRA, France), Laporte Marie-Angélique (Bioversity, France), Larmand Pierre (IRD, France), Letellier Thomas (INRA, France), Lucas Hélène (INRA, France), Pommier Cyril (INRA, France), Protonotarios Vassilis (Agro-Know, Greece), Quesneville Hadi (INRA, France), Shrestha Rosemary (INRA, France), Subirats Imma (FAO of the United Nations, Italy), Aravind Venkatesan (IBC, France), Whan Alex (CSIRO, Australia) Co-chairs: Esther Dzalé Yeumo Kaboré (INRA, France), Richard Allan Fulss (CIMMYT, Mexico)

Thank you!